A novel data augmentation method and a data-driven prediction model for surface flashover at gas-solid interfaces under nanosecond pulses.

Chuyu Sun, Haiyang Wang, Gefei Wang, Gang Wu, Jiahui Yin, Tao Huang, Shengchang Ji

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Article in Scientific reports, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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5 · Who and what money

Authors and funding

7 authors.

Chuyu SunNorthwest Institute of Nuclear Technology, Xi'an, China.

Haiyang WangNorthwest Institute of Nuclear Technology, Xi'an, China.

Gefei WangNorthwest Institute of Nuclear Technology, Xi'an, China.

Gang WuNorthwest Institute of Nuclear Technology, Xi'an, China.

Jiahui YinNorthwest Institute of Nuclear Technology, Xi'an, China.

Tao HuangNorthwest Institute of Nuclear Technology, Xi'an, China.

Shengchang JiXi'an Jiaotong University, Xi'an, China. jsc@xjtu.edu.cn.

Funding

the National Key Laboratory of Intense Pulsed Radiation Simulation and Effect NKLIPR2420

6 · The paper itself

Abstract

Surface flashover at gas-solid interfaces is a critical factor in electromagnetic pulse simulator reliability. To accurately predict flashover events over a wide surface distance range (15-500 mm), this paper develops a machine-learning-based classification model. Three experimental platforms with output ranging from ± 80 kV to ± 2000 kV were constructed, yielding 1245 valid data samples covering various surface distances, voltage polarities, gas pressures, electrode configurations, and voltage waveforms. To address severe class imbalance (flashover proportion > 85%) under certain experimental conditions, a data augmentation method based on the three-parameter Weibull distribution is proposed: non-flashover samples are generated by sampling below a low cumulative probability threshold (U

Indexed as

Data augmentationFeature importance analysisSurface flashoverWeibull distribution

Identifiers

PMID42203810

DOI10.1038/s41598-026-53820-w

PMCPMC13444085

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.